The impact of perturbation intensity schedule on improvements in reactive balance control in young adults: an experimental study
Bibliographic record
Abstract
ABSTRACT Background Reactive balance training (RBT) uses unanticipated perturbations to improve balance reactions and prevent falls. Intensity and predictability of perturbations may affect the generalizability of RBT, but their relative contributions remain unclear. This study aimed to compare the effects of three RBT schedules with differing intensity and predictability on participants’ balance recovery following untrained perturbations, and to determine perceived difficulty and challenge of the training schedules. Methods Participants were 36 healthy young adults (20-35 years). Participants were randomly assigned to one of three RBT intensity schedules (fixed high intensity, low-to-high intensity, and variable intensity). Training took place on a motion platform that delivered perturbations in varying directions (forward, backward, left, and right) and intensities across five trial blocks. Balance reactions (number of recovery steps) pre- and post-training, and electrodermal responses, and perceived difficulty during training were collected. Statistical analyses compared post-training outcomes between training groups, controlling for the baseline value. Results Participants took fewer steps and decreased the proportion of multi-step reactions pre- to post-training (p<0.0001), with no significant between-group differences. Perceived exertion decreased significantly across training blocks in the fixed-high group and increased significantly in the low-to-high group. Electrodermal responses declined across all groups between training blocks 1 and 3 (p=0.017). Conclusions Improvements in step reactions to untrained perturbations did not differ between training groups, highlighting the importance of multi-directional variability over specific intensity schedules for enhancing balance recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".